Whether high sensitivity C-reactive protein (hs-CRP) has a causal effect on coronary heart disease (CHD) is unclear. This study investigated the causal effect of hs-CRP on CHD risk using Mendelian Randomization (MR) analysis.CRP polymorphisms and hs-CRP. Survival receiver operator characteristic curve method was used to explore the cut-off of hs-CRP on CHD incidence. Cox regression model was applied to detect the association of hs-CRP with CHD by calculating the hazard ratio (HR) and 95% confidence interval (CI). Rs1205 and rs876537 in CRP were selected as instrumental variables in MR analysis.A total of 3802 subjects were recruited in the follow-up study. Linear regression model was used to evaluate the relationship between 4 person-years). Hs-CRP was significantly increased among rs1205 and rs876537 genotypes with r values of 0.064 and 0.066, respectively. Hs-CRP 1.08 mg/L was identified as the cut-off value with a maximum value of sensitivity and specificity on prediction of CHD. Participants with ≥1.08 mg/L of hs-CRP has a higher risk of CHD incidence than that of participants with < 1.08 mg/L, the adjusted HR (95% CI) was 1.69 (1.11–2.60) with a P value of 0.016. No significant casual association was observed between hs-CRP and CHD with a P value of 0.777.During a median follow-up time of 5.01 years, 98 CHD incidence was identified (47.03/10The association between hs-CRP and CHD is unlikely to be causal, hs-CRP might be a predictor for incidence of CHD in general population. Coronary heart disease (CHD) is one of the leading global cause of death and disability. The current situation of rapid aging in China makes CHD become the 4th leading cause of mortality . The incInflammation promotes endothelial cell damage and atherogenesis, is an important risk factor for CHD in clinical . As a faMendelian randomization (MR) studies utilize genetic variants (such as he-CRP) as instrumental variables (IVs) to investigate possible causal relationship between exposure and outcomes through an intermediate trait . The domCRP that influence hs-CRP levels, and use the concept of MR to improve understanding of the possible causal relationship of hs-CRP levels with CHD.The aim of the current study was to identify common single nucleotide polymorphisms (SNPs) in n = 50), elder participants (n = 94) and missing measurement of hs-CRP (n = 373), 3802 subjects were finally enrolled in the follow-up study.A total of 4222 participants were drawn from the community hypertension survey in Yixing city, China, 2009 . After wTo access the incidence of disease status in the follow-up study, both concentrated and household surveys were conducted. Data collected from the local hospitals, Centers for Disease Control and Prevention, community health service centers and social security center was further inspected to reduce the information bias. During a median follow-up time of 5.01 years, 98 new-onset CHDs were recorded.All participants were interviewed and underwent physical examinations and laboratory tests. The demographic characteristics of the participants were obtained by trained research staff though a standard questionnaire. The weight, height and 3 blood pressure measurements were obtained from each participant by trained and certified observers according to a standard protocol.Written informed consent was obtained from all participants. The research protocol was approved by the ethics committee of Nanjing Medical University and written informed consent was obtained from all subjects during epidemiological interviews.CRP gene was located on chromosome 1q23.2  and spans 2.3 kbps and contains 4 exons. We searched the SNPs covered CRP gene from the upstream 5 kb to the downstream 2 kb and selected tagging SNPs (tagSNPs) from the database of the Chinese Han population in Beijing, China of the International Hap MAP Project . All tagSNPs were selected with a minor allele frequency (MAF) ≥ 0.05 and linkage disequilibrium (LD) r2 ≥ 0.8. We also applied a functional candidate strategy to select potential functional SNPs on the bioinformatics effect prediction website . Finally, rs1205(C > T), rs1073715(C > T), rs876537(C > T) and rs2808630(C > T) were selected. The specific biological information was summarized in the Additional file The Blood samples were taken from participants after an over-night fasting (> 10 h), adding ethylenediamine tetraacetic acid (EDTA)-containing receptacles. The blood samples were centrifuged at 3500 rpm to separate the serum and stored at − 20 °C. Serum hs-CRP, total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C) and glucose (GLU) were measured by an automatic biochemical analysis system of Siemens .Following recommendations from the American Centers for Disease Control and the American Heart Association , the parDNA was extracted using a standard phenole-chloroform method. Genotyping was performed using the TaqMan allelic discrimination assay in 384-well plates on the platform of 7900HT Real-time polymerase chain reaction (PCR) System . The primers and probes were designed using Primer Express Oligo Design software v2.0 (ABI PRISM). Genotyping results were determined using SDS 2.3 Allelic Discrimination Software (Applied Biosystems). Meanwhile, each plate was included blank samples as negative controls for the genotyping quality confirmation. The successful call rates of SNPs rs10737175, rs1205, rs2808630 and rs2966449 were 98.44, 98.31, 98.39 and 98.41%, respectively.χ2) test was performed to compare the proportion of hypertension, T2DM, smoking and drinking between HRG and NG group. Linear regression model was used to analyze the correlation between the variations of CRP gene and hs-CRP. Besides, we used survival receiver operator characteristic curve (SROC) to explore the cut-off values of hs-CRP with CHD incidence. Cox proportional hazard regression model was applied to evaluate the association of CRP gene with CHD by calculating hazard ratio (HR) and 95% confidence interval (CI).Unpaired Student’s t-test was used to test the differences in of all the quantitative variables among groups presented as means ± standard deviation (SD). Chi-square (OR) and 95%CI. A two-tailed P value of 0.05 was defined as the cut-off for statistical significance. The SROC and MR analysis were undertaken using the R Packages of “survivalROC” and “Mendelian Randomization”.The MR analysis for exploring the causal effect of hs-CRP on the risk of CHD applying inverse-variance weighted (IVW) methods, where the SNPs are deemed as IVs. The basic principle of MR analysis for the current study was shown in Fig. P < 0.05). Overall, these characteristics were adjusted as confounding factors in the multivariate regression analysis.The demographic characteristics of participants were summarized in Table 4 person-years. No significant associations were observed between each of the four SNPs with CHD in the whole population. After adjusted for age, gender, BMI, HDL-C, LDL-C, TC, TG, smoking, drinking, hypertension and T2DM, the results were still not significant  was 2.05 (1.36–3.09) with a P value of 0.001. But, the result was still not significant after adjusted for cofounders, the adjusted HR (95% CI) was 1.54 (0.99–2.36) with a P value of 0.051.The incidence density of CHD for HRG and NG group were 71.08/104 vs 32.97/104 person-years), the adjusted HR (95% CI) was 1.69 (1.11–2.60) with a P value of 0.016. Detailed results were displayed in Table The SROC analysis showed that the highest sensitivity and specificity of cut-off point for hs-CRP to predict CHD incidence was 1.08 mg/L, the sensitivity and specificity were 60.2 and 59.1%, respectively  was 1.04 (0.67–1.62) with a P value of 0.855. The result remained negative after adjustment for age, gender, BMI, TC, TG, HDL-C, LDL-C, smoking, drinking, T2DM and hypertension, the adjust OR (95% CI) was 1.07 (0.69–2.18) with a P value of 0.777.The causal effect was evaluated by using MR analysis assessing the association of genetically predicted hs-CRP with CHD risk Table . The cruCRP rs1205 and rs876537 were correlated with serum hs-CRP level, but no causal effect of elevated hs-CRP on CHD risk was observed. Although the association between hs-CRP and CHD is unlikely to be causal, hs-CRP might be a predictor for incidence of CHD in general population.The present study firstly applied MR design to explore the casual effect of hs-CRP and CHD risk in Chinese Han population. The main results showed that Epidemiological studies have shown that plasma levels of hs-CRP are a strong independent predictor of risk of future CVDs , 18. As CRP locus and CHD were found, arguing against a causal role for CRP in atherosclerosis [CRP rs1205 as one of IVs and indicated that CRP was unlikely to be even a modest causal factor in CHD [Previous large sample size studies showed that no association of variants in the clerosis . Anotherr in CHD . As a nor in CHD . These fAlthough American Centers for Disease Control and the American Heart Association has recommended hs-CRP 1.0 mg/L as a cut-off value to identify individuals who is under a high risk of inflammatory reaction , no studThe current study investigated the cause effect of hs-CRP and CHD risk in a prospective cohort study. The strengths of this study should be noted. First, for the expensive cost of hs-CRP measurement, limited MR studies have carried out to evaluate the association of hs-CRP with CHD in a prospective cohort study. All the participants with a measurement of hs-CRP was enrolled in the follow-up study, the relative large sample size in our study substantially enhanced the statistical power, providing an objective and credible result on he-CRP and CHD. Second, it was firstly to explore the cut-off value of hs-CRP on predicting CHD incidence in Chinese Han population.CRP gene expression can be up-regulated, leading to cascades that would increase the hs-CRP levels. However, our data might not accurately represent the fundamental of gene expression related to hs-CRP.Several limitations were existed in the current study. First of all, the common SNPs that significantly correlated with hs-CRP are not derived from genome-wide association study data. In addition, the causal effect of hs-CRP on sub-types of CHD were not investigated. Besides, the pleiotropy effect of SNPs in MR analysis was not evaluated. Last but not least, during acute events, the CRP rs1205 and rs876537 variation were correlated with hs-CRP level.In conclusion, Although the association between hs-CRP and CHD is unlikely to be causal, hs-CRP might be a predictor for incidence of CHD in general population.Additional file 1.Table S1. Bioinformatics Analysis of 4 selected SNPs in CRP Gene. Table S2. Stratification analysis of CRP gene polymorphisms and CHD. Table S3. Partial correlation analysis of CRP variation with hs-CRP. Figure S1. Survival ROC analysis for hs-CRP and CHD incidence.


RNA-protein 3D complex structure prediction is still challenging. Recently, a template-based approach PRIME is proposed in our team to build RNA-protein 3D complex structure models with a higher success rate than computational docking software. However, scoring function of RNA alignment algorithm SARA in PRIME is size-dependent, which limits its ability to detect templates in some cases.Herein, we developed a novel RNA 3D structural alignment approach RMalign, which is based on a size-independent scoring function RMscore. The parameter in RMscore is then optimized in randomly selected RNA pairs and phase transition points (from dissimilar to similar) are determined in another randomly selected RNA pairs. In tRNA benchmarking, the precision of RMscore is higher than that of SARAscore  with phase transition points. In balance-FSCOR benchmarking, RMalign performed as good as ESA-RNA with a non-normalized score measuring RNA structural similarity. In balance-x-FSCOR benchmarking, RMalign achieves much better than a state-of-the-art RNA 3D structural alignment approach SARA due to a size-independent scoring function. Take the advantage of RMalign, we update our RNA-protein modeling approach PRIME to version 2.0. The PRIME2.0 significantly improves about 10% success rate than PRIME.Based on a size-independent scoring function RMscore, a novel RNA 3D structural alignment approach RMalign is developed and integrated into PRIME2.0, which could be useful for the biological community in modeling protein-RNA interaction.The online version of this article (10.1186/s12864-019-5631-3) contains supplementary material, which is available to authorized users. RNA plays important roles in many biological processes such as gene regulation, subcellular location and splicing. High-throughput global mapping of RNA duplexes with near base-pair resolution reveals that RNA interacts with RNA and RNA-binding proteins using higher order architectures in living cell . Most ofIn this manuscript, we introduce an RNA structural alignment approach based on RMscore, which is a size independent scoring function to measure RNA structural similarity. Firstly, we reveal the liner relationship between the logarithmic length of RNA and the logarithmic radius of gyration (Rg) of RNA. At the same time, the aligned correlation coefficient (ACC) describing the relationship between RMSD and Rg also has a complex function relation with the RNA length. Combining these function relations, a length slightly independent scoring function RMscore is determined (the RMscore only slightly decreases as the length increases). Then RMscore is applied to two randomly selected independent datasets to optimize parameters and determine the transition point from similar to dissimilar. With the transition point, RMscore performs better than SARAscore , 13, 22 We download RNA structure coordinates from PDB  website Total 2557 RNA structures and their complexes from PDB are separated by chains. 3775 RNA chains are kept expect the RNA structures in mmcif format. PDB-3775 represents all RNA structures in PDB. The relationships between the Rg of RNA and the RNA length are explored in this dataset.ACC in proteins describing the relationship between RMSD and Rg is reported in . In ordeWe randomly selected 0.3 million RNA pairs from all-to-all alignment of RNA chains in PDB-3775 to optimize parameters in RMscore. The alignment of the paired RNA is generated by needle . This daWe randomly chose 0.1 million RNA pairs from all-to-all pair of RNA chains in PDB-3775 to determine the phase transition. The alignment of the paired RNA is aligned by SARA , which ihttp://ndbserver.rutgers.edu/). We extract one RNA chain from one structure or its complex. This process outputs 175 RNA chains. tRNA pairs are then constructed through all-to-all pairwise alignment by SARA.We downloaded all tRNA structures from NDB  (http://http://structure.biofold.org/sara/datasets.html), which is constructed from SCOR [FSCOR , 13 is drom SCOR  to benchwww.rnabinding.com/RMalign/RMalign.html. The vary cut-offs are tried, because it is still unknown which value is appropriate to cluster the RNA structures.Structural similarity is used as evaluation in protein structural alignment protocol. However, RNA function is used as the metric in benchmarking in balance-FSCOR. So, we construct balance-x-FSCOR to benchmark RNA structural alignment approach employing the RNA structural similarity RMScore as a metric. Firstly, FSCOR is clustered by RMalign with different RMscore cut-offs  to construct the x-FSCOR. Then 1000 positive and negative pairs are randomly selected from all-to-all pairs of x-FSCOR. These datasets are named as the balance-x-FSCOR. If the number of positive or negative pairs is less than 1000, the dataset contains less pairs. The structural classes with various cut-offs of 419 RNA chains in FSCOR can be downloaded from The unbound set is used to compare the performance of PRIME  and PRIM0.4 where N is the number of residues in a protein. Adopting a similar strategy with protein, we investigate the relationship between Rg of the RNA and its length. Simply, all RNA structures are represented with C3’ atoms. The average log Rg located in the same length bins is calculated. After calculating Rg of all RNAs in PDB-3775, we observe that a scaling law Rg ∝ N0.39 for RNA.The Rg of protein is an important metric to describe the compactness of protein. Previous studies , 31 abouA (RB) is the Rg for structure A (B), rAi(rBi) is the coordinate vector after superposition.The ACC has a function correlation with the Rg of protein and RMSD . The Rg In order to reveal the relation between the ACC and aligned nucleic acids, we take a similar strategy used in TMalign . FirstlyN is the length of fragment in the fragment-pairs.Where LInspired by a protein scoring function TM-score, we introduce a size-independent scoring function RMscore describing the similarity of RNA structure. For an RNA alignment, RMscore is defined as:N is the length of average of target and query RNA, LT is the length of aligned nucleic acids to the target structure, di is the distance between the ith pair of aligned nucleic acids and d0 is a scale to normalize the length effect. ‘Max’ denotes the maximum after optimal superposition. For different scoring strategies, a different d0 is adopted. In RMscore, a length dependent d0 is adopted. Similarity to TM-score, d0 is estimated by multiplying 0 and the length can be estimated from Rg ∝ N0.39 and eq. (2).Where L0 is optimized to smooth the curve).Here the constant compensation (set as 0.6) is introduced to smooth the curve when the RMscore is optimized in random pairs-0.3 M , gapless structural alignment (GSA) and alignment combining SSA and GSA, respectively.1.1 RNA SSA. We totally consider five secondary structural states of RNA calculated by X3DNA . They ar1.2 RNA GSA. The secondary initial structural alignment is GSA. In TMalign, the TM-score is used as the comparison metric. In RMalign, we employ the RMscore to select the best alignment.1.3 Alignment combining SSA and GSA. In the third initial alignment, we combine the SSA and GSA with the scoring matrix that is a half/half combination of secondary score matrix  and distance score matrix .We obtain the alignments based on the step 1. In this step, the alignment is scored by the RMscore. First, the alignment is divided into fragment of length  where LN is the length of alignment. The complete alignment fragment is then rotated by the convergent rotation matrix which is obtained by continuously superposing the nts of fragment by Kabsch algorithm with distance less than 5 Å. Secondary, the RMscore is calculated with eq.We obtain the RMscore rotation matrix from step 2. In this step, the aligned RNA structure is rotated by the RMscore rotation matrix. And then a scoring similarity matrix S is calculated according to the Eq.In this step, the alignment is scored with all the aligned nts. And the result is output.ij is the distance of the ith nt in RNA 1 and the jth nt in RNA 2 under the RMscore superposition. The value of d0 is determined by eq. min is the length of smaller RNA.Comparing with TMalign, two processes are modified. Firstly, in the secondary type of initial alignment, RNA secondary structure is calculated by X3DNA . SecondlTo test the performance of RMalign in RNA function classification and compare with ESA-RNA, a balance-FSCOR based on FSCOR is constructed. The same structures may have different functions. And the purpose of RNA structural alignment approach is to detect the structural similarity. So, we also benchmark RMalign in balance-x-FSCOR. The AUC value is used as the metric to measure the performance.We previous developed an approach PRIME  to predi0 is assigned to 5 in the definition of RMscore) changes with the length. To overcome the shortcoming of length-dependent scoring function for comparing RNA structures, we propose a size-independent scoring function RMscore  to measure the RNA structural similarity. In order to obtain the formula of RMscore like TM-score, firstly, we reveal that log Rg of RNA has liner relation (R2 = 0.91) with the logarithmic RNA length  with the number of aligned nucleic acids  is 10% improved than before. Benchmarking on RNA function prediction, RMalign also shows a very high precision with AUC 0.95, which is as good as ESA. The study provides a foundation for novel RNA structural alignment approach in a size-independent way, applicable to the protein-RNA complex structure modeling and RNA function and fold classification. On the basis of the results we designed and implemented an RNA alignment tool, which should be useful for the biological community interested in RNA structural studies.Additional file 1:Figure S1.RNA structural similarity vs the length of RNA. The length of RNA is plotted against RMscore with a different compensation  to smooth the average RMscore at a smaller length, in random pairs 0.3 M. RNA alignment is accomplished by needle in EMBOSS package. Raw-RMscore means that d0 is assigned to 5 Å in RMscore definition. The compensation is chosen as 0.6 that will result in the smoothest curver. (PDF 227 kb)Additional file 2:Figure S2.RMSD vs RNA structural similarity. SARAscore (A) and RMscore are plotted against RMSD in 0.1 million randomly selected pairs, RNA structural alignments are accomplished by SARA. The insets show the fraction of RNA-RNA pairs with RMSD <= 5 Å are plotted with 0.05 bins to show the phase transition from dissimilar RNA pairs to the similar pairs. (PDF 413 kb)Additional file 3:Figure S3.Distribution of RMScore (B) and SARAscore (A). All-to-all pairs are categorized into Low-RMScore (5 Å < RMSD <= 10 Å), High-RMscore (RMSD <= 5 Å) or Non-RMscore (RMSD > 10 Å). The same category criterion is applied for SARAscore. The RMscores corresponding to the peak value are separated clear, which can be used to distinguish similarity or dissimilarity RNA pairs, but SARAscores corresponding to the peak value are close. (PDF 178 kb)Additional file 4:Figure S4.Distribution of RMscore benchmarking in balance-FSCOR. Positive pairs are RNA pairs with the same functions. Negative pairs are RNA pairs with different functions. The figure shows that most negative pairs have lower RMscore and most positive pairs have higher RMscore. (PDF 196 kb)Additional file 5:Figure S5.F-measure, ACC, MCC vs RMscore cut off. F-measure, ACC (accuracy) and MCC are plotted against RMscore cut off selected to predict the positive or negative pairs in benchmarking on balance-FSCOR. (PDF 359 kb)Additional file 6:Figure S6.A false positive example of RMalign benchmarking in balance-SCOR. 5msf:S (yellow) is superposed on 2iz8:R (cyan) by RMalign. These two RNA structures are very similar (RMscore = 0.837). But they are belonged to two distinct function classes in FSCOR . (PDF 236 kb)Additional file 7:Figure S7.Benchmarking of RMalign and SARA in balance-x-FSCOR. (PDF 143 kb)


This study aimed to measure muscle activity and motion kinematics during chair-based exercises under submerged and non-submerged conditions. Twelve healthy men performed chair-based standing and sitting movements. Surface electrodes were attached at the tibialis anterior, gastrocnemius, rectus femoris, biceps femoris, rectus abdominis, and erector spinae. The ankle, knee, and hip joint angles and forward inclination angle of the trunk segment in the sagittal plane were calculated. The mean muscle activities during both movements in the submerged condition for the entire motion were lower than those in the non-submerged condition except in the tibialis anterior and biceps femoris during the sitting movement . However, the integrated muscle activity in submerged conditions was similar or higher to that in non-submerged conditions during both movements, except for the rectus femoris. This was mainly due to the increased duration of motion . The hip joint flexion at the beginning and end of movement and forward inclination angles of the trunk segment at the beginning of the movement in the submerged condition were larger than those in the non-submerged condition during both movements . Reduced or similar muscle activity but similar or higher muscular effort was observed in the submerged condition for all the muscles except the rectus femoris, with the upper body inclined forward. These findings could have beneficial implications for the prescription of exercise and rehabilitation regimens. Chair-based standing and sitting movements are among the most fundamental activities of daily living (ADLs) besides walking , 2. DeteSubmerged exercise has become popular for rehabilitation and functional training. In the submerged state, buoyancy reduces stress associated with gravity during exercise, while water resistance increases the driving force required to move the limbs by human . RecentlWhile many biomechanical studies on walking in the submerged state have been conducted, little has been done to investigate movements involved in standing and sitting from a chair despite these movements being used in aquatic rehabilitation and functional exercise programs –19. As lWith the above in mind, the purpose of this study was to measure muscle activity and movement kinematics during chair-based standing and sitting movements with the participant either submerged or not submerged. The hypothesis was that the muscle activity of the lower limb would be reduced in submerged conditions compared with non-submerged conditions. With regards to motion kinematics, we expected to find a similar pattern but a somewhat more flexed posture of the lower-limb joints and trunk segment in submerged conditions compared with non-submerged conditions during both chair standing and sitting movements, as is seen during walking , 14, 20.Twelve healthy men participated in the present study. Their means ± standard deviations (SDs) of age, height, weight, and body mass index were 29.8 ± 8.8 years, 174.3 ± 5.0 cm, 67.8 ± 5.5 kg, and 22.3 ± 1.9 kg∙m-2, respectively. They had no orthopedic diseases that could affect chair-based standing/sitting. We obtained written informed consent from all participants before conducting the study. The Human Ethics Committee of Chiba Institute of Technology approved the protocol.The experiment was conducted using a made-to-order water tank  with a glass window on one side for exercises in the submerged condition and a concrete floor adjacent to the water tank for exercises in the non-submerged condition. Participants sat on a steel box without a back rest and performed five consecutive sit-to-stand and stand-to-sit movements with three sets. The height of the steel box was 40 cm, as described in a previous study , 14, 21.The muscle activity of the right side of the tibialis anterior (TA), medial head of the gastrocnemius (GAS), rectus femoris (RF), long head of the biceps femoris (BF), rectus abdominis (RA), and erector spinae (ES) was measured during the movements. A wireless EMG sensor  was placed on the muscle belly and the data were collected at a 1-kHz sampling rate. The attachment locations were determined based on the Anatomical Guide for EMG ), phase 2 (P2) comprised the beginning of knee joint extension to the reversal of trunk segment flexion–extension, and phase 3 (P3) comprised the reversal of trunk segment flexion–extension to the end of sit-to-stand in the sit-to-stand movement. In the stand-to-sit movement, P1 comprised the beginning of stand-to-sit to the reversal of trunk segment flexion–extension, P2 comprised the reversal of trunk segment flexion–extension to the end of knee joint flexion (threshold of knee joint angular velocity was the same), P3 comprised the end of knee joint flexion to the end of stand-to-sit. These phase distinction methods were referred from a previous study [The EMG and motion capture data were collected from the beginning to the end of each sit-to-stand and stand-to-sit movement based on the angular velocity of the trunk segment. The threshold value was set at < or > 0.087 rad∙s-1 (5° per second) according to a previous study . The colus study . After dThe durations for the sit-to-stand movement were 1.85 ± 0.31 s in the non-submerged condition and 2.67 ± 0.43 s in the submerged condition for the entire motion. The durations of each phase in non-submerged and submerged conditions were P1: 0.53 ± 0.16 s and 0.87 ± 0.37 s, P2: 0.34 ± 0.07 s and 0.38 ± 0.17 s, and P3: 0.98 ± 0.16 s and 1.41 ± 0.20 s, respectively. In the stand-to-sit movement, durations in non-submerged and submerged conditions were 2.01 ± 0.25 s and 2.82 ± 0.30 s for the entire motion, P1: 0.94 ± 0.15 s and 1.40 ± 0.25 s, P2: 0.50 ± 0.15 s and 0.75 ± 0.30 s, and P3: 0.57 ± 0.15 s and 0.67 ± 0.31 s, respectively. Significantly longer durations (p < 0.05) were detected in the submerged condition than in the non-submerged condition during the sit-to-stand movement for the entire motion (44.3%), P1 (62.3%), and P3 (44.1%). In the stand-to-sit movement, significantly longer durations (p < 0.05) were recorded in the submerged than non-submerged condition for the entire motion (39.9%), P1 (48.3%), and P2 (49.1%).The mEMG values of each muscle during each movement are shown in The iEMG values are expressed in The mean angles at the moment of beginning and ending each movement in both conditions shown in This is the first study to investigate muscle activity during both sit-to-stand and stand-to-sit movements with the participants submerged in water. To our knowledge, only one previous study  has inveThe present study divided exercises into sit-to-stand and stand-to-sit movements. The duration for both movements in the non-submerged condition seemed reasonable when compared with the results of the study by Walaszek et al.  and someCuesta-Vargas et al.  comparedIn the present study, mEMG values of the GAS muscle were significantly lower in submerged than non-submerged conditions for the entire motion of both movements, in the stand-to-sit movement for P1, and in the sit-to-stand movement for P3. A previous study  supportsThe mEMG values of the RF muscle were significantly lower in the submerged condition than the non-submerged condition for the entire motion in both movements, which is in agreement with data of a previous study relating to the GAS muscle . One reaWith respect to the BF muscle, a significantly lower mEMG value in the submerged condition than in the non-submerged condition was observed in the sit-to-stand movement, as reported by a previous study . This waThe mEMG value of the RA muscle in the present study showed fairly low activity in both movements and conditions, although significantly lower mEMG values were seen in the submerged condition than in the non-submerged condition during both movements. A previous study reported higher mEMG values in the submerged condition than in the non-submerged condition during the sit-to-stand movement . In addiOther directions of research are the implementation of pace setting and assessment of the effects of chair surface height and water depth. Previous studies on walking and running classified pace as slow, normal, or fast , 21, 27;Humans can perform standing and sitting exercises on a chair with safety for acute injuries or failure during exercise in an aquatic environment , 30. PreIn conclusion, the present findings suggest that chair-based exercise with the participant submerged in water reduces muscle activity, especially for sit-to-stand motion which was mostly in according to the previous study, but leads to similar or greater muscular effort for the lower and trunk muscles except for the RF due to the long motion duration in the submerged condition. In addition, the higher muscle activity has been observed especially in BF during stand-to-sit motion in the submerged condition because of buoyancy acted on like water resistance. Moreover, there is flexion of the hip joint and forward inclination of the trunk segment when submerged in water in both standing/sitting motion. Our results present important information which could be beneficial for the exercise and rehabilitation instructors and/or patients with regards to the design and prescription of training regimens.


Diabetes mellitus type 1 and type 2 are linked to higher prevalence and occurrences of depression. Internet-based depression- and diabetes-specific cognitive behavioral therapies (CBT) can be effective in reducing depressive symptom severity and diabetes-related emotional distress. The aim of the study was to test whether disease-specific severity indicators moderate the treatment outcome in a 6-week minimally guided web-based self-help intervention on depression and diabetes (GET.ON Mood Enhancer Diabetes (GET.ON M.E.D.)) and to determine its effectiveness in a nonsuicidal severely depressed subgroup. N = 253) comparing GET.ON M.E.D. to an online psychoeducation control group was used to test disease-specific severity indicators as predictors/moderators of a treatment outcome. Changes in depressive symptom severity and treatment response were examined in a nonsuicidal severely depressed subgroup .  Randomized controlled trial- (RCT-) based data (pprf6 = 0.01), higher levels of depression , and lower HbA1c (pprpo = 0.04) predicted changes in depressive symptoms. No severity indicator moderated the treatment outcome. Severely depressed participants in the intervention group showed a significantly greater reduction in depressive symptom severity : 1.39-2.96) than the control condition , with a between-group effect size of dprpo = 1.05 (95% CI: 0.11-1.98). Treatment response was seen in significantly more participants in the intervention  compared to the control group (N = 40) = 4.44; p < 0.02). At the 6-month follow-up, effects were maintained for depressive symptom reduction  but not treatment response.  Major depressive disorder diagnosis at the baseline ( Disease-specific severity indicators were not related to a differential effectiveness of guided self-help for depression and diabetes. Clinical meaningful effects were observed in nonsuicidal severely depressed individuals, who do not need to be excluded from web-based guided self-help. However, participants should be closely monitored and referred to other treatment modalities in case of nonresponse. Prevalence of diabetes mellitus in the general population is high. In 2014, according to the World Health Organization, 422 million adults worldwide (8.5%) were suffering from diabetes [Treatment options are available for MDD and diabetes separately, but only few specialized treatments exist that take both diabetes and depression into account. The majority of “double troubled” individuals remain untreated for depression, calling for integration of depression treatment in routine diabetes care . Internedfu = 0.29; 95% CI: 0.17-0.40) compared to a waitlist control group as well as a greater reduction in diabetes-related emotional distress. The intervention had no beneficial effect on glycemic control. Similar evidence was provided by Nobis et al. [d = 0.89; 95% CI: 0.64-1.15) and diabetes-related emotional distress  at posttreatment when compared to an online psychoeducation intervention. Effects of improvements in depressive symptom severity were sustained at the 6-month follow-up  [In the last decade, a large number of studies have documented the effectiveness of IMIs in mental disorders and specifically MDD –6. To th0.6-1.1) .Despite the evidence for effective IMIs for depression  and evolKnowing whether more severely depressed individuals with diabetes may profit or not from an IMI is key to provide adequate, tailored treatment options with low risk of harm and high chance of treatment success. To evaluate treatment modifiers, van Bastelaar et al.  comparedIn the present study, we aim to evaluate the effectiveness of a minimally guided web-based self-help intervention for comorbid depression in individuals with diabetes with regard to treatment predictors and moderators and specifically with regard to participants with nonsuicidal severe depression. The primary research questions were the following: (1) Do disease-specific severity indicators  predict and/or moderate the effectiveness of GET.ON M.E.D. compared to an online psychoeducation control group (CG)? (2) What are the specific effects of GET.ON M.E.D. in nonsuicidal severely depressed individuals when compared to online psychoeducation?N = 253) examining the effectiveness of a minimally guided self-help intervention for individuals with diabetes mellitus type 1 or type 2 and comorbid depression compared to an online psychoeducation control condition [All secondary analyses were conducted with data from a randomized controlled trial (ondition –9. To exondition  were meaPatients were eligible for the study if they (a) were at least 18 years old, (b) met criteria for diabetes mellitus type 1 or type 2 ICD-10 , (c) hadParticipants in the intervention group (IG) had access to the online intervention GET.ON M.E.D. . The onParticipants in the control group (CG) had access to an online psychoeducation based on the guidelines for MDD of the German Physicians Association , 23. PsyThe primary outcome of the primary study was depressive symptom severity measured with the German version of the Center for Epidemiological Studies-Depression (CES-D) . The iteMDD at the Baseline (SCID). To assess the diagnostic status of the participants, the structured clinical interview for DSM-IV (SCID) was conducted at the baseline [baseline , 25. TraMost Recent Blood Glucose Level (1cHbA). HbA1c as the indicator of overall diabetes control was assessed at the baseline by self-report and referred back to the previous six to eight weeks. Healthy people have an HbA1c under 39 mmol/mol or 5.7%. Values from 48 mmol/mol (6.5%) and up indicate diabetes. The German Diabetes Society recommends to aim for HbA1c under 58 mmol/mol (7.5%) for type 1 [r type 1  and betwr type 1 .Diabetes-Related Emotional Distress. Current emotional distress related to living with diabetes was assessed by the Problem Areas in Diabetes [α = 0.83). In this study, Cronbach's alpha was 0.84.Diabetes  scale shDiabetes . It consDiabetes . The valIn addition to depressive symptom severity, the following moderators were assessed:Prediction, multiple moderation, and nonsuicidal severely depressed subgroup analyses were conducted. All analyses were performed with IBM Statistics 24. Missing data were imputed using a Markov chain Monte Carlo  multivar1c) on the effectiveness of the intervention, multiple moderation analysis (MMA) with the SPSS macro PROCESS by Hayes [To determine the effects of disease-specific severity indicators , medium effects (d = 0.5), and large effects (d = 0.8) [χ2) test.Inclusion was defined by a cutoff of CES − D ≥ 40 (through converted BDI-II cutoff of 29 for severe depression), which refers to severe depression. An analysis of variance with simple effects was used to compare outcomes between groups at posttreatment and at 6-month follow-up. Results were reported as mean within- and between-group differences and as Cohen's ct sizes  (and thect sizes ). Interpd = 0.8) . Improved = 0.8)  and as ad = 0.8) , the reld = 0.8)  with GETn = 159; 62.8%) with an average age of 50.7 (SD = 11.7). The majority of participants  had a midlevel education and were Caucasians . A total of 44.6% (n = 113) participants were reported to be diagnosed with diabetes mellitus type 1, and the other 55.3% (n = 140) were diagnosed with diabetes mellitus type 2. 43.8% (n = 111) took medication for diabetes; in 21.3% (n = 55), the medication was insulin. The average time since their diabetes was diagnosed was between 5 and 10 years (range: 0.25 years–over 10 years). A total of 24.9% (n = 63) were diagnosed with diabetes-related complications, such as high blood pressure and nerve damage ; however, improvement was predicted from the baseline to follow-up . Furthermore, higher levels of depressive symptom severity at the baseline were associated with greater reduction in depressive symptom severity to posttreatment  and follow-up . Lower levels of HbA1c were associated with greater improvement in depressive symptom severity from the baseline to posttreatment  but not from the baseline to follow-up . Diabetes-related emotional distress did predict the treatment outcome neither at posttreatment  nor at follow-up . For further information, see Baseline MDD did not predict improvement in depressive symptom severity from the baseline to posttreatment  and were investigated in the nonsuicidal severely depressed subgroup analysis. It revealed that nonsuicidal severely depressed participants in the intervention group benefitted substantially from the intervention in terms of reduction of depressive symptom severity  = 4.47, p = 0.017, d = 0.67, CI: 0.03-1.30) when compared to the control group. In the intervention group, simple effect analysis showed a statistically significant reduction in depressive symptom severity from the baseline to posttreatment  and to 6-month follow-up , which was greater than in the control group . Between-group effect size was d = 1.05 (CI: 0.11-1.98) at posttreatment and d = 0.71 (CI: 0.19-1.61) at 6-month follow-up.In total, 40 participants  fulfilled criteria for severe depression  = 5.013; p = 0.012). The difference between the groups at the follow-up was not statistically significant (χ2 (1) = 2.50; p = 0.056, ns.). To achieve one additional treatment response as compared to the control group, 2.9 participants have to be treated with this intervention.The reliable change index at posttreatment showed that in the IG, 15 participants (75%) improved at posttreatment. Achieved changes were maintained at the 6-month follow-up with twelve (60%) participants maintaining achieved changes from posttreatment and two (10%) participants who improved from posttreatment to follow-up. At posttreatment in the CG, eight participants (40%) improved, which was maintained at the 6-month follow-up by seven (35%) participants. Two (10%) participants improved from posttreatment to follow-up. The difference between the groups at posttreatment was statistically significant (χ2 (2) = 4.44; p = 0.017). From posttreatment to follow-up, one (5%) participant of each group showed a positive treatment response (ns.). To achieve one additional positive treatment response, five participants have to be treated with this intervention.Positive treatment response in terms of reduction of depressive symptom severity (CES-D reduction of 50%) at posttreatment was higher in the intervention group with four participants (20%), whereas no participant (0%) in the CG showed a positive treatment response. This difference was statistically significant (χ2 (1) = 2.10; p = 0.07, ns.). To achieve one additional near-to-symptom-free status at follow-up treatment, ten participants have to be treated.Near-to-symptom-free status was not achieved in either groups at posttreatment; two (10%) participants in the IG achieved near-to-symptom-free status at the 6-month follow-up as compared to none (0%) in the CG. Near-to-symptom-free status was not statistically significantly different between the groups at follow-up assessment (χ2 (1) = 1.02; p = 0.15, ns.). At the follow-up, three participants (15%) in the IG experienced a deterioration as compared to one (5%) in the control group; this difference was not statistically significant (χ2 (1) = 1.11; p = 0.14, ns.).Symptom deterioration did not take place in the intervention group at posttreatment (0%). In the CG, one participant (5%) did experience deterioration. This difference was not statistically significant . This is shown by the NNT of 2.9 in the clinical subgroup which is comparable to the NNT of 2.2 in the effectiveness trial [Our findings on the effectiveness of GET.ON M.E.D. in the nonsuicidal severely depressed subgroup are supported by studies which show that Internet-based self-help interventions can be effective in clinical populations , 41. Resss trial , that pa1c in multiple moderation analyses. We found HbA1c to be a significant predictor but not a moderator. It should however be taken into account that most participants' values for the HbA1c were already in the normal range at the baseline, so that baseline variance in HbA1c values may be too low to detect potential effects on changes in depression.To the best of our knowledge, previous studies did not include the HbAd = 0.89 for differences in depression severity at posttreatment. Therefore, the sample size allowed us to detect moderators with a medium to large effect but may be still too small to detect moderators with only small effects. Third, no clinical interviews took place at posttreatment or 6-month follow-up. Therefore, changes in the diagnosis of MDD could not be analyzed. Fourth, the data used in this trial was collected in a previous trial, so that only predictors and moderators which were included in the original design could be evaluated.The following limitations have to be considered: First, participants with a potential risk of acute suicidal ideation were excluded, thereby limiting the findings to a nonsuicidal severely depressed subgroup. This limits the generalizability of our results as severe depression is often accompanied by risk of acute suicidal ideation. There is no scientific evidence on specific exclusion criteria for participants within online interventions, although exclusion of potential participants with specific conditions may be reasonable . It remains unclear, which exclusion criteria should be considered with regard to a specific population. Second, participants were recruited from the general population, meaning generalizability to other settings in which patients seek help in routine clinical mental health care might be limited. Furthermore, this study was not a priori planned to detect potential moderators –44 and tThe results from this study support evidence that nonsuicidal severely depressed individuals can profit from low-threshold interventions and extend the findings to nonsuicidal severely depressed individuals with a somatic comorbidity. In our sample, 6.5% of all participants were excluded from the study due to an elevated level of suicidal ideation, increasing the likelihood of suicidal behavior. Seemingly, individuals interested in IMIs may have a lower association between severe depression and suicidal ideation compared to individuals that prefer traditional treatment options. Safety management may therefore be a valid option, if the IMI may be implemented in routine care. However, future research should include severely depressed individuals with suicidal ideation to examine which safety management should be used for suicidal behavior prevention within IMIs.For severe depression, current treatment guidelines , 46 recoAlthough IMIs do not cause side effects on a somatic level, participation might be accompanied by negative effects. Such negative effects might include negative changes in relationships with friends and family and negative changes in effect due to the IMI itself , 55. HowFrom a health economic perspective, there are no studies available that show the cost-effectiveness of antidepressant medication and IMIs. There are several studies that show the cost-effectiveness of an antidepressant medication . There iSince a direct comparison of pharmacological treatment and IMIs in terms of clinical and cost-effectiveness is not yet available, it is important to investigate which kind of treatment is beneficial for which group of patients. IMIs for patients with diabetes should be considered, when pharmacological antidepressant treatment is not an option. However, not every severely depressed participant profits from an IMI, so further research is needed to better identify severely depressed participants that are likely to profit from low-threshold interventions such as IMIs and to identify nonresponders to be able to refer them to other treatment formats.In sum, it can be concluded that a minimally guided online self-help intervention is effective in reducing depressive symptom severity in participants with lower as well as higher levels of depressive symptom severity. This indicates that nonsuicidal severely depressed participants with somatic comorbidities do not need to be excluded from online interventions as partaking in such interventions can be beneficial, especially within stepped care approaches.